It is a problem in the field of natural resource management to perform population and/or biological monitoring to uniquely identify an individual subject in a target population. In particular, while work has been done to differentiate among various species in a study area, in order to produce a gross count of the members of each species in the study area, there is a lack of any system that can automatically and accurately differentiate among the individual members of a particular species in a target population.
It is well known that the species in various taxa have external characteristics that are useful in uniquely identifying an individual member of the species. This process of identifying an individual member of the species is commonly done on a manual basis, more commonly in the management of mammals, where the herd managers can visually distinguish among members of a particular herd. This process is experientially based in that the herd manager over time remembers various characteristics that distinguish members of the managed herd. However, this knowledge is typically not recorded in any formal manner and is therefore not simply transferable to others. In addition, the knowledge base of a particular herd is not extensible to any other herd and the data collection and member recognition process must be laboriously reinitiated when a herd manager encounters a new herd or even additions to an existing herd. Finally, the identification process is not rigorous, in that it is subject to the personal determinations of the herd manager, may not be based on scientifically accurate distinctions, and may be limited to only gross identifying characteristics that are easily recognized.
In the case of very large populations of subjects, where the data collection and subject distinguishing processes are beyond a simple visual and human-based memory process, there is presently no method to enable the recognition of an individual member of the population. In these populations, the process in identifying the individual must be automatic, rapid and accurate and extensible to any size population or any population among a larger set of populations. In addition, any recognition process must be able to account for temporal and growth changes in the members of the population, such as seasonal variations and changes associated with aging. In addition, in order to distinguish among members of a very large population, a scientifically rigorous process must be used and is typically based on subtle and/or complex differentiable characteristics.